# Stronger is not better: Better Augmentations in Contrastive Learning for Medical Image Segmentation

Source: [Stronger is not better: Better Augmentations in Contrastive Learning for Medical Image Segmentation](<https://www.sota2.com/research/paper/6937ad176cfd488761a4e863>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-12T05:02:33.257Z

Authors: Azeez Idris, Abdurahman Ali Mohammed, Samuel Fanijo

Published: 2025-11-30T22:42:49.000Z

## Abstract

Self-supervised contrastive learning is among the recent representation learning methods that have shown performance gains in several downstream tasks including semantic segmentation. This paper evaluates strong data augmentation, one of the most important components for self-supervised contrastive learning's improved performance. Strong data augmentation involves applying the composition of multiple augmentation techniques on images. Surprisingly, we find that the existing data augmentations do not always improve performance for semantic segmentation for medical images. We experiment with other augmentations that provide improved performance.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2512.05992>)

[PDF](<https://arxiv.org/pdf/2512.05992.pdf>)